Closed-loop control of product properties is becoming increasingly important in forming technology research and enables users to counteract unavoidable uncertainties in semi-finished product properties and process environments. Therefore, closed-loop controlled forming processes are considered to have the potential to reduce tolerances on desired product properties, resulting in consistent qualities. The achievement of associated increases in robustness and reliability is linked to enormous requirements, which in particular include the inline recording of the product properties to be controlled and the subsequent adaptation of the process control through the targeted derivation of manipulated variables. The present paper uses the example of an air bending process to show how the bending angle can be controlled camera-based and how springback can be compensated within a stroke by recording force signals and subsequently predicting the loaded bending angle using machine learning algorithms. The results show that the combined application of camera-based control and machine learning assisted springback compensation leads to highly accurate bending angles, whereby the results strongly depend on the machine learning algorithms and associated data transformation processes used.

产品属性的闭环控制在成型技术研究中变得越来越重要，并使用户能够抵消半成品属性和工艺环境中不可避免的不确定性。因此，闭环控制成型工艺被认为有可能降低所需产品性能的公差，从而获得一致的质量。鲁棒性和可靠性的相关提高的实现与巨大的要求相关，其中特别包括要控制的产品属性的在线记录以及随后通过有针对性地导出操纵变量来调整过程控制。本文以空气弯曲过程为例，展示如何基于摄像头控制弯曲角度，以及如何通过记录力信号并随后使用机器学习算法预测负载弯曲角度来补偿行程内的回弹。结果表明，基于摄像头的控制和机器学习辅助回弹补偿的结合应用可以实现高精度的弯曲角度，而结果在很大程度上取决于所使用的机器学习算法和相关数据转换过程。